Detection of Anonymising Proxies Using Machine Learning

Shane Miller, Kevin Curran, Tom Lunney

Source Title: International Journal of Digital Crime and Forensics (IJDCF)13(6)

ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781799867531|DOI: 10.4018/IJDCF.286756

MLA

Miller, Shane, et al. "Detection of Anonymising Proxies Using Machine Learning." IJDCF vol.13, no.6 2021: pp.1-17. http://doi.org/10.4018/IJDCF.286756

APA

Miller, S., Curran, K., & Lunney, T. (2021). Detection of Anonymising Proxies Using Machine Learning. International Journal of Digital Crime and Forensics (IJDCF), 13(6), 1-17. http://doi.org/10.4018/IJDCF.286756

Chicago

Miller, Shane, Kevin Curran, and Tom Lunney. "Detection of Anonymising Proxies Using Machine Learning," International Journal of Digital Crime and Forensics (IJDCF) 13, no.6: 1-17. http://doi.org/10.4018/IJDCF.286756

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Abstract

Network Proxies and Virtual Private Networks (VPN) are tools that are used every day to facilitate various business functions. However, they have gained popularity amongst unintended userbases as tools that can be used to hide mask identities while using websites and web-services. Anonymising Proxies and/or VPNs act as an intermediary between a user and a web server with a Proxy and/or VPN IP address taking the place of the user’s IP address that is forwarded to the web server. This paper presents computational models based on intelligent machine learning techniques to address the limitations currently experienced by unauthorised user detection systems. A model to detect usage of anonymising proxies was developed using a Multi-layered perceptron neural network that was trained using data found in the Transmission Control Protocol (TCP) header of captured network packets